| 1. |
- Bachimanchi, Harshith, et al.
(författare)
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Microplankton life histories revealed by holographic microscopy and deep learning
- 2022
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Ingår i: eLife. - 2050-084X. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- The marine microbial food web plays a central role in the global carbon cycle. However, our mechanistic understanding of the ocean is biased toward its larger constituents, while rates and biomass fluxes in the microbial food web are mainly inferred from indirect measurements and ensemble averages. Yet, resolution at the level of the individual microplankton is required to advance our understanding of the microbial food web. Here, we demonstrate that, by combining holographic microscopy with deep learning, we can follow microplanktons throughout their lifespan, continuously measuring their three-dimensional position and dry mass. The deep-learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended time periods. This permits us to reliably estimate growth rates, both in terms of dry mass increase and cell divisions, as well as to measure trophic interactions between species such as predation events. The individual resolution provides information about selectivity, individual feeding rates, and handling times for individual microplanktons. The method is particularly useful to detail the rates and routes of organic matter transfer in micro-zooplankton, the most important and least known group of primary consumers in the oceans. Studying individual interactions in idealized small systems provides insights that help us understand microbial food webs and ultimately larger-scale processes. We exemplify this by detailed descriptions of micro-zooplankton feeding events, cell divisions, and long-term monitoring of single cells from division to division.
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| 2. |
- Midtvedt, Benjamin, et al.
(författare)
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Fast and Accurate Nanoparticle Characterization Using Deep-Learning-Enhanced Off-Axis Holography
- 2021
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Ingår i: ACS Nano. - : American Chemical Society (ACS). - 1936-0851 .- 1936-086X. ; 15:2, s. 2240-2250
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Tidskriftsartikel (refereegranskat)abstract
- Characterization of suspended nanoparticles in their native environment plays a central role in a wide range of fields, from medical diagnostics and nanoparticle-enhanced drug delivery to nanosafety and environmental nanopollution assessment. Standard optical approaches for nanoparticle sizing assess the size via the diffusion constant and, as a consequence, require long trajectories and that the medium has a known and uniform viscosity. However, in most biological applications, only short trajectories are available, while simultaneously, the medium viscosity is unknown and tends to display spatiotemporal variations. In this work, we demonstrate a label-free method to quantify not only size but also refractive index of individual subwavelength particles using 2 orders of magnitude shorter trajectories than required by standard methods and without prior knowledge about the physicochemical properties of the medium. We achieved this by developing a weighted average convolutional neural network to analyze holographic images of single particles, which was successfully applied to distinguish and quantify both size and refractive index of subwavelength silica and polystyrene particles without prior knowledge of solute viscosity or refractive index. We further demonstrate how these features make it possible to temporally resolve aggregation dynamics of 31 nm polystyrene nanoparticles, revealing previously unobserved time-resolved dynamics of the monomer number and fractal dimension of individual subwavelength aggregates.
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| 3. |
- Midtvedt, Benjamin, et al.
(författare)
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Quantitative digital microscopy with deep learning
- 2021
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Ingår i: Applied Physics Reviews. - : AIP Publishing. - 1931-9401. ; 8:1
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Forskningsöversikt (refereegranskat)abstract
- Video microscopy has a long history of providing insight and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time-consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we introduce software, DeepTrack 2.0, to design, train, and validate deep-learning solutions for digital microscopy. We use this software to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking, and characterization, to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.0 can be easily customized for user-specific applications, and thanks to its open-source, object-oriented programing, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.
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| 4. |
- Midtvedt, Benjamin, et al.
(författare)
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Single-shot self-supervised object detection in microscopy
- 2022
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Object detection is a fundamental task in digital microscopy, where machine learning has made great strides in overcoming the limitations of classical approaches. The training of state-of-the-art machine-learning methods almost universally relies on vast amounts of labeled experimental data or the ability to numerically simulate realistic datasets. However, experimental data are often challenging to label and cannot be easily reproduced numerically. Here, we propose a deep-learning method, named LodeSTAR (Localization and detection from Symmetries, Translations And Rotations), that learns to detect microscopic objects with sub-pixel accuracy from a single unlabeled experimental image by exploiting the inherent roto-translational symmetries of this task. We demonstrate that LodeSTAR outperforms traditional methods in terms of accuracy, also when analyzing challenging experimental data containing densely packed cells or noisy backgrounds. Furthermore, by exploiting additional symmetries we show that LodeSTAR can measure other properties, e.g., vertical position and polarizability in holographic microscopy.
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| 5. |
- Midtvedt, Daniel, et al.
(författare)
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Size and Refractive Index Determination of Subwavelength Particles and Air Bubbles by Holographic Nanoparticle Tracking Analysis
- 2020
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Ingår i: Analytical Chemistry. - : American Chemical Society (ACS). - 0003-2700 .- 1520-6882. ; 92:2, s. 1908-1915
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Tidskriftsartikel (refereegranskat)abstract
- Determination of size and refractive index (RI) of dispersed unlabeled subwavelength particles is of growing interest in several fields, including biotechnology, wastewater monitoring, and nanobubble preparations. Conventionally, the size distribution of such samples is determined via the Brownian motion of the particles, but simultaneous determination of their R1 remains challenging. This work demonstrates nanoparticle tracking analysis (NTA) in an off-axis digital holographic microscope (DHM) enabling determination of both particle size and RI of individual subwavelength particles from the combined information about size and optical phase shift. The potential of the method to separate particle populations is demonstrated by analyzing a mixture of three types of dielectric particles within a narrow size range, where conventional NTA methods based on Brownian motion alone would fail. Using this approach, the phase shift allowed individual populations of dielectric beads overlapping in either size or RI to be clearly distinguished and quantified with respect to these properties. The method was furthermore applied for analysis of surfactant-stabilized micro- and nanobubbles, with RI lower than that of water. Since bubbles induce a phase shift of opposite sign to that of solid particles, they were easily distinguished from similarly sized solid particles made up of undissolved surfactant. Surprisingly, the dependence of the phase shift on bubble size indicates that only those with 0.15-0.20 mu m radius were individual bubbles, whereas larger bubbles were actually clusters of bubbles. This label-free means to quantify multiple parameters of suspended individual submicrometer particles offers a crucial complement to current characterization strategies, suggesting broad applicability for a wide range of nanoparticle systems.
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| 6. |
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| 7. |
- Pineda, Jesus, et al.
(författare)
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Geometric deep learning reveals the spatiotemporal features of microscopic motion
- 2023
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Ingår i: Nature Machine Intelligence. - : Springer Science and Business Media LLC. - 2522-5839. ; 5, s. 71-82
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Tidskriftsartikel (refereegranskat)abstract
- Despite recent improvements in microscopy acquisition methods, extracting quantitative information from biological experiments in crowded conditions is a challenging task. Pineda and colleagues propose a geometric deep-learning-based framework for automated trajectory linking and dynamical property estimation that is able to effectively deal with complex biological scenarios. The characterization of dynamical processes in living systems provides important clues for their mechanistic interpretation and link to biological functions. Owing to recent advances in microscopy techniques, it is now possible to routinely record the motion of cells, organelles and individual molecules at multiple spatiotemporal scales in physiological conditions. However, the automated analysis of dynamics occurring in crowded and complex environments still lags behind the acquisition of microscopic image sequences. Here we present a framework based on geometric deep learning that achieves the accurate estimation of dynamical properties in various biologically relevant scenarios. This deep-learning approach relies on a graph neural network enhanced by attention-based components. By processing object features with geometric priors, the network is capable of performing multiple tasks, from linking coordinates into trajectories to inferring local and global dynamic properties. We demonstrate the flexibility and reliability of this approach by applying it to real and simulated data corresponding to a broad range of biological experiments.
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| 8. |
- Grauer, J., et al.
(författare)
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Active droploids
- 2021
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Active matter comprises self-driven units, such as bacteria and synthetic microswimmers, that can spontaneously form complex patterns and assemble into functional microdevices. These processes are possible thanks to the out-of-equilibrium nature of active-matter systems, fueled by a one-way free-energy flow from the environment into the system. Here, we take the next step in the evolution of active matter by realizing a two-way coupling between active particles and their environment, where active particles act back on the environment giving rise to the formation of superstructures. In experiments and simulations we observe that, under light-illumination, colloidal particles and their near-critical environment create mutually-coupled co-evolving structures. These structures unify in the form of active superstructures featuring a droplet shape and a colloidal engine inducing self-propulsion. We call them active droploids-a portmanteau of droplet and colloids. Our results provide a pathway to create active superstructures through environmental feedback. Active matter can spontaneously form complex patterns and assemblies via a one-way energy flow from the environment into the system. Here, the authors demonstrate that a two-way coupling, where active particles act back on the environment can give rise to novel superstructures, named as active droploids.
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| 9. |
- Wang, Gan, et al.
(författare)
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Nanoalignment by critical Casimir torques
- 2024
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Ingår i: NATURE COMMUNICATIONS. - 2041-1723. ; 15:1
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Tidskriftsartikel (refereegranskat)abstract
- The manipulation of microscopic objects requires precise and controllable forces and torques. Recent advances have led to the use of critical Casimir forces as a powerful tool, which can be finely tuned through the temperature of the environment and the chemical properties of the involved objects. For example, these forces have been used to self-organize ensembles of particles and to counteract stiction caused by Casimir-Liftshitz forces. However, until now, the potential of critical Casimir torques has been largely unexplored. Here, we demonstrate that critical Casimir torques can efficiently control the alignment of microscopic objects on nanopatterned substrates. We show experimentally and corroborate with theoretical calculations and Monte Carlo simulations that circular patterns on a substrate can stabilize the position and orientation of microscopic disks. By making the patterns elliptical, such microdisks can be subject to a torque which flips them upright while simultaneously allowing for more accurate control of the microdisk position. More complex patterns can selectively trap 2D-chiral particles and generate particle motion similar to non-equilibrium Brownian ratchets. These findings provide new opportunities for nanotechnological applications requiring precise positioning and orientation of microscopic objects. Recent advances in manipulating microscopic objects involve utilizing critical Casimir forces, controllable via temperature and chemical properties. By demonstrating the efficiency of critical Casimir torques, the authors enable precise alignment of microscopic objects on nanopatterned substrates.
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